Development of a hydrocyclone separation efficiency model using artificial neural networks
Abstract
A hydrocyclone is an apparatus that is widely used throughout the mineral processing industry. Usually the hydrocyclone is used for the classification, desliming or dewatering of slurries. It is inexpensive, application-efficient and easily employed within different processes. When classifying slurries, the separation efficiency (or the performance) of the hydrocyclone is described by the cut-size and the sharpness of classification coefficient, collectively referred to as a partition curve. These separation efficiency indicating parameters cannot be measured in real-time and are thus quantified by utilising models. Most of the available models are derived from experimentally obtained data and are therefore empirical in nature. Over the last two decades researchers have started employing alternative techniques in order to develop a separation efficiency model. These include updated empirical models, black-box approaches and Computational Fluid Dynamics (CFD) studies. The main goal of this study was to develop an Artificial Neural Network (ANN) model that estimates the cut-size and sharpness of classification coefficient by using experimentally attained data. Such a model can be used in predicting the separation efficiency parameters in real-time, a soft sensor, subsequently lending itself to possible control of the hydrocyclone’s performance in real-time.
It is important to note that an ANN’s usefulness is directly related to the data that are used to train it. It was therefore imperative that high quality data were collected. Using Experimental Design (ED) a structured set of experiments, which included the entire operating range of the hydrocyclone, are described. An experimental procedure was planned and executed in order to obtain the necessary samples in an organised fashion. The experiments were taken on a 100 mm hydrocyclone test rig and the slurries consisted of fine silica with a maximum volumetric solid concentration of 3.125 %. The collected samples were then analysed using the Malvern Particle Size Analyser 2000. Finally the analysed data could be processed accordingly and then used to develop a specified ANN.
In order to determine the best possible ANN, many different variations were trained and then tested using data unknown to the ANN and comparing the obtained estimates to experimental data. Some of the ANN inputs include the pressure, volumetric solid concentration and the spigot opening diameter. To determine whether more inputs to the ANN might deliver better estimations, additional hydrocyclone variables (such as overflow flow rate and angle of discharge) were also used as inputs. The outputs were the separation efficiency indicating parameters. Firstly the cut-size and sharpness of classification coefficient as separate outputs were determined and secondly the combined outputs thereof. In order to determine whether the ANN application is warranted, the ANN results were compared to a well-known empirical model from literature. The study is concluded by meticulously reviewing the work that was done and the results that were attained, especially referring to the use of an ANN for estimating a hydrocyclone’s separation efficiency compared to existing models from literature. It is evident that the more hydrocyclone variables that are used as ANN inputs, the better the ANN estimations become. Limited literature is available on estimating the sharpness of classification coefficient and this might be because of complex correspondence to the hydrocyclone variables. This study shows that the sharpness of classification coefficient estimations performs poorly, irrespective of the ANN architecture.
Some future work could focus on incorporating instrumentation on the test rig, in order to log certain measurements in real-time. This will also be useful for control purposes when a hydrocyclone model is used along with a control-valve. Another aspect that might be useful to investigate is the real-time processing of the angle of discharge. For this study the angle of discharge photos were only processed after the experiments were concluded. An on-line image processing aspect might be an interesting addition to the on-line measurements.
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